Systems and methods for data packet metadata stabilization

ABSTRACT

Systems and methods for accelerated stabilization of data packet metadata are disclosed herein. The system can include a memory having a content database and a user profile database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers. The one or more servers can: retrieve data packet metadata for a data packet; determine that the data packet metadata is unstable; identify a set of potential recipients of the data packet; select one of the set of potential recipients as the recipient of the data packet; provide the data packet to the recipient of the data packet; receive a response from the recipient to the provided data packet; and automatically update the data packet metadata based on the response received from the recipient.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/236,103, filed on Aug. 12, 2016, and entitled “SYSTEMS AND METHODSFOR DATA PACKET METADATA STABILIZATION,” which claims the benefit ofU.S. Provisional Application No. 62/320,213, filed on Apr. 8, 2016, andentitled “ADAPTIVE PATHWAYS AND COGNITIVE TUTORING”; this applicationclaims the benefit of U.S. Provisional Application No. 62/211,156, filedon Aug. 28, 2015, and entitled “DATA-ENABLED SUCCESS AND PROGRESSIONSYSTEM”; this application is a Continuation-in-Part of U.S. applicationSer. No. 14/927,115, filed on Oct. 29, 2015, now U.S. Pat. No.10,218,630, and entitled “SYSTEM AND METHOD FOR INCREASING DATATRANSMISSION RATES THROUGH A CONTENT DISTRIBUTION NETWORK”, which claimsthe benefit of U.S. Provisional Application No. 62/072,910, filed onOct. 30, 2014; this application is a Continuation-in-Part of U.S.application Ser. No. 14/927,145, filed on Oct. 29, 2015, now U.S. Pat.No. 10,027,740, and entitled “SYSTEM AND METHOD FOR INCREASING DATATRANSMISSION RATES THROUGH A CONTENT DISTRIBUTION NETWORK WITHCUSTOMIZED AGGREGATIONS”, which claims the benefit of U.S. ProvisionalApplication No. 62/073,751, filed on Oct. 31, 2014; this application isa Continuation-in-Part of U.S. application Ser. No. 14/927,170, filed onOct. 29, 2015, and entitled “CONTENT DATABASE GENERATION”, which claimsthe benefit of U.S. Provisional Application No. 62/072,914, filed onOct. 30, 2014; this application is a Continuation-in-Part of U.S.application Ser. No. 14/928,152, filed on Oct. 30, 2015, now U.S. Pat.No. 9,667,321, and entitled “PREDICTIVE RECOMMENDATION ENGINE”, whichclaims the benefit of U.S. Provisional Application No. 62/073,814, filedon Oct. 31, 2014, the entirety of each of which is hereby incorporatedby reference herein.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, the communications protocols to organize network traffic, thenetwork's size, topology and organizational intent. In most cases,communications protocols are layered on (i.e. work using) other morespecific or more general communications protocols, except for thephysical layer that directly deals with the transmission media.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for acceleratedstabilization of data packet metadata. The system includes a memory. Thememory can include: a content database including a plurality of datapackets and metadata identifying an attribute of an associated datapacket; and a user profile database including user history dataidentifying an attribute of an associated user. In some embodiments,each of the plurality of data packets is associated with uniquemetadata, and in some embodiments, each user is associated with uniqueuser history data. The system can include a user device. The user devicecan include: a first network interface that can exchange data via acommunication network; and a first I/O subsystem that can convertelectrical signals to user interpretable outputs via a user interface.The system can include one or more servers. The one or more servers can:identify a data packet that includes content for delivery to a userdevice; retrieve data packet metadata from the content database of thememory, which data packet metadata identifies a difficulty level of thedata packet; determine that the data packet metadata is unstable;identify a set of potential recipients of the data packet; select one ofthe set of potential recipients as the recipient of the data packetbased on a correspondence between the data packet metadata and user datafor the recipient; provide the data packet to the recipient of the datapacket; receive a response from the recipient to the provided datapacket; and automatically update the data packet metadata based on theresponse received from the recipient.

In some embodiments, determining that the data packet metadata isunstable includes retrieving a stability threshold, which stabilitythreshold identifies a minimum number of received user responsessubsequent to the providing of the data packet. In some embodiments,determining that the data packet metadata is unstable includesextracting a value indicative of the number of received responsessubsequent to the providing of the data packet from the data packetmetadata; and comparing the stability threshold and the value indicativeof the number of received responses subsequent to the providing of thedata packet from the metadata.

In some embodiments, the one or more servers include a responseprocessor that can translate the received response into an observable.In some embodiments, translating the received response into anobservable includes evaluating the received response to determine if thereceived response is a desired response. In some embodiments, updatingthe data packet metadata includes updating a model of the difficulty ofthe data packet. In some embodiments, the model of the difficulty of thedata packet is a piecewise Gaussian distribution model.

In some embodiments, the one or more servers can repeatedly: identifythe set of potential recipients of the data packet; select one of theset of potential recipients as the recipient of the data packet based ona correspondence between the data packet metadata and user data for therecipient; provide the data packet to the recipient of the data packet;receive a response from the recipient to the provided data packet; andautomatically update the data packet metadata based on the responsereceived from the recipient, until the one or more servers determinethat the data packet metadata is stable. In some embodiments, the one ormore servers can add an indicator of stability to the data packetmetadata when the one or more servers determine that the data packetmetadata is stable.

In some embodiments, the one or more servers can: automatically generatean alert including an indicator of the stability of the data packetmetadata when the one or more servers determine that the data packetmetadata is stable; and send the alert to a supervisor device. In someembodiments, the alert includes computer code to direct the supervisordevice to automatically launch an application that displays an indicatorof the metadata stability.

One aspect of the present disclosure relates to a method for acceleratedstabilization of data packet metadata. The method includes: (a)identifying a data packet that includes content for delivery to a userdevice; (b) retrieving data packet metadata from a memory including acontent library database; (c) determining that the data packet metadatais unstable; (d) identifying a set of potential recipients of the datapacket; (e) selecting one of the set of potential recipients as therecipient of the data packet based on a correspondence between the datapacket metadata and user data for the recipient; (f) providing the datapacket to the recipient of the data packet; (g) receiving a responsefrom the recipient to the provided data packet; and (h) automaticallyupdating the data packet metadata based on the response received fromthe recipient. In some embodiments, the data packet metadata identifiesa difficulty level of the data packet.

In some embodiments, the method includes, repeating steps (a)-(h) untilthe data packet metadata is stable. In some embodiments, selecting oneof the set of potential recipients as the recipient of the data packetincludes: (i) selecting one potential recipient of the set of potentialrecipients for analysis; (ii) determining a load level for the selectedone potential recipient of the set of potential recipients; and (iii)adding a value indicative of acceptability when a predicted load levelis less than a load threshold.

In some embodiments, selecting one potential recipient of the set ofpotential recipients as the recipient of the data packet includesgenerating a predicted load value that is based off of the determinedload level for the selected one potential recipient of the set ofpotential recipients update to include the load effect of receipt of thedata packet. In some embodiments, the method includes: retrieving theload threshold; comparing the predicted load value to the loadthreshold; and removing the selected one potential recipient of the setof potential recipients from the set of potential recipient when thepredicted load level is greater than the load threshold.

In some embodiments, the method includes repeating steps (i)-(iii) whenit is determined that there are additional potential recipients in theset of potential recipients that have not been selected in step (i). Insome embodiments, the method includes designating a recipient associatedwith the value indicative of acceptability when it is determined thatthere are no additional potential recipients in the set of potentialrecipients that have not been selected in step (i). In some embodiments,determining that the data packet metadata is unstable includesretrieving a stability threshold that identifies a minimum number ofreceived user responses subsequent to the providing of the data packet.In some embodiments, determining that the data packet metadata isunstable includes extracting a value indicative of the number ofreceived responses subsequent to the providing of the data packet fromthe data packet metadata; and comparing the stability threshold and thevalue indicative of the number of received responses subsequent to theproviding of the data packet from the metadata.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of user device andsupervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of a computingstack.

FIGS. 9A-9C are schematic illustrations of embodiments of communicationand processing flow of modules within the content distribution network.

FIG. 10A is a flowchart illustrating one embodiment of a process fordata management.

FIG. 10B is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 11 is a flowchart illustrating one embodiment of a process forautomatic difficulty determination of the data packet and placement ofthat data packet in the content network.

FIG. 12 is a flowchart illustrating one embodiment of a process for datapacket metadata stabilization,

FIG. 13 is a flowchart illustrating one embodiment of a process forselecting recipient of the data packet.

FIG. 14 is a flowchart illustrating one embodiment of a process forautomatically updating data packet metadata and/or automaticallyupdating a data packet model.

FIG. 15 is an illustration of one embodiment of a piece-wise Gaussiandistribution.

FIG. 16 is an illustration of a plurality of piecewise Gaussiandistributions.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

With reference now to FIG. 1 , a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing unit, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination of computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provide access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3 . Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming systems, business or homeappliances, and/or a personal messaging devices, capable ofcommunicating over network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such as awireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1 , the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including his or her user device 106, contentresources accessed, and interactions with other user devices 106. Thisdata may be stored and processed by the user data server 114, to supportuser tracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1 , the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include several featuresthat can communicate with one or several components of the contentdistribution network 100 including, for example, with one or several ofthe user devices 106 and/or with one or several of the supervisordevices 110. In some embodiments, this communication can include thetransmission of a signal from the navigation system 122 which signal isreceived by one or several components of the content distributionnetwork 100 and can be used to determine the location of the one orseveral components of the content distribution network 100.

With reference to FIG. 2 , an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1 , and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser-based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2 , various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XML,encryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such aspublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1 ,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3 , an illustrative set of data stores and/ordata store servers is shown, corresponding to the data store servers 104of the content distribution network 100 discussed above in FIG. 1 . Oneor more individual data stores 301-311 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-311, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-311 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user mayhave one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, thestudent's learning style can be any learning style describing how thestudent best learns or how the student prefers to learn. In oneembodiment, these learning styles can include, for example,identification of the student as an auditory learner, as a visuallearner, and/or as a tactile learner. In some embodiments, the dataidentifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, these datapackets in the content library database 303 can be linked to form anobject network. In some embodiments, these data packets can be linked inthe object network according to one or several prerequisiterelationships that can, for example, identify the relative hierarchyand/or difficulty of the data objects. In some embodiments, thishierarchy of data objects can be generated by the content distributionnetwork 100 according to user experience with the object network, and insome embodiments, this hierarchy of data objects can be generated basedon one or several existing and/or external hierarchies such as, forexample, a syllabus, a table of contents, or the like. In someembodiments, for example, the object network can correspond to asyllabus such that content for the syllabus is embodied in the objectnetwork.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network, also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user;relation to a common subject, topic, skill, or the like; creation from acommon set of source material such as source data packets; or the like.In some embodiments, the content aggregation can comprise a grouping ofcontent comprising the presentation portion that can be provided to theuser in the form of, for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets that they can be, for example, organized in object network. Insome embodiments, the one or several content aggregations can be eachcreated from content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and, in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network can be organized according to one or several tasksidentified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309,can store information relating to one or several predictive models. Insome embodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Performance Factor Analysis (PFA), or the like. Theevidence model can, in some embodiments, be customizable to a userand/or to one or several content items. Specifically, one or severalinputs relating to the user and/or to one or several content items canbe inserted into the evidence model. These inputs can include, forexample, one or several measures of user skill level, one or severalmeasures of content item difficulty and/or skill level, or the like. Thecustomized evidence model can then be used to predict the likelihood ofthe user providing desired or undesired responses to one or several ofthe content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a user such as a student-user failing to achieve a desiredoutcome such as, for example, failing to correctly respond to one orseveral data packets, failure to achieve a desired level of completionof a program, for example in a pre-defined time period, failure toachieve a desired learning outcome, or the like. In some embodiments,the risk probability can identify the risk of the student-user failingto complete 60% of the program.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of the program such as, for example, a completion stage and/orcompletion status of the program. In one embodiment, for example, thefirst model function can be associated with a first completion status,the second model function can be associated with a second completionstatus, the third model function can be associated with a thirdcompletion status, and the fourth model function can be associated witha fourth completion status. In some embodiments, these completionstatuses can be selected such that some or all of these completionstatuses are less than the desired level of completion of the program.Specifically, in some embodiments, these completion statuses can beselected to all be at less than 60% completion of the program, and morespecifically, in some embodiments, the first completion status can be at20% completion of the program, the second completion status can be at30% completion of the program, the third completion status can be at 40%completion of the program, and the fourth completion status can be at50% completion of the program. Similarly, any desired number of modelfunctions can be associated with any desired number of completionstatuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a student-user's progress through a program.In some embodiments, the student-user's progress can be compared to oneor several status trigger thresholds, each of which status triggerthresholds can be associated with one or more of the model functions. Ifone of the status triggers is triggered by the student-user's progress,the corresponding one or several model functions can be selected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program status parameters. In some embodiments, each of the modelfunctions can use the same program status parameters, and in someembodiments, the model functions can use different program statusparameters. In some embodiments, the model functions use differentprogram status parameters when at least one of the model functions usesat least one program status parameter that is not used by others of themodel functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several students. In someembodiments, this model can identify a single skill level of a studentand/or a range of possible skill levels of a student. In someembodiments, this statistical model can identify a skill level of astudent-user and an error value or error range associated with thatskill level. In some embodiments, the error value can be associated witha confidence interval determined based on a confidence level. Thus, insome embodiments, as the number of student interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

A threshold database 310, also referred to herein as a thresholddatabase, can store one or several threshold values. These one orseveral threshold values can delineate between states or conditions. Inone exemplary embodiment, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education-related data, consumer sales data,health-related data, and the like. Illustrative external dataaggregators 311 may include, for example, social networking web servers,public records data stores, learning management systems, educationalinstitution servers, business servers, consumer sales data stores,medical record data stores, etc. Data retrieved from various externaldata aggregators 311 may be used to verify and update user accountinformation, suggest user content, and perform user and contentevaluations.

With reference now to FIG. 4 , a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering. This embodiment allows reporting timely information thatmight be of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: contentprovided to users; content consumed by users; responses provided byusers; user skill levels; content difficulty levels; next content forproviding to users; etc. The internal components 402-408 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The content management server102 can opt to allow third parties to use internally or externallygathered information that is aggregated within the server 102 bysubscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 238 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time or along another time line. There is a definedAPI between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith response reported that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like SCTP andUDP could be used in some embodiments to intake the gatheredinformation. A protocol such as SSL could be used to protect theinformation over the TCP connection. Authentication and authorizationcan be performed to any user devices 106 and/or supervisor deviceinterfacing to the server 102. The security and/or integration hardware410 receives the information from one or several of the user devices 106and/or the supervisor devices 110 by providing the API and anyencryption, authorization, and/or authentication. In some cases, thesecurity and/or integration hardware 410 reformats or rearranges thisreceived information.

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. Specifically, in some embodiments,the messaging bus 412 can receive and output information from at leastone of the packet selection system, the presentation system, theresponse system, and the summary model system. In some embodiments, thisinformation can be output according to a “push” model, and in someembodiments, this information can be output according to a “pull” model.

As indicated in FIG. 4 , processing subscribers are indicated by aconnector to the messaging bus 412, the connector having an arrow headpointing away from the messaging bus 412. Only data streams within themessaging queue 412 that a particular processing subscriber hassubscribed to may be read by that processing subscriber if received atall. Gathered information sent to the messaging queue 412 is processedand returned in a data stream in a fraction of a second by the messagingqueue 412. Various multicasting and routing techniques can be used todistribute a data stream from the messaging queue 412 that a number ofprocessing subscribers have requested. Protocols such as Multicast ormultiple Unicast could be used to distribute streams within themessaging queue 412. Additionally, transport layer protocols like TCP,SCTP and UDP could be used in various embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of message in a particularcategory. For example, a data stream can comprise all of the datareported to the messaging bus 412 by a designated set of components. Oneor more processing subscribers could subscribe and receive the datastream to process the information and make a decision and/or feed theoutput from the processing as gathered information fed back into themessaging queue 412. Through the CC interface 338 a developer can searchthe available data streams or specify a new data stream and its API. Thenew data stream might be determined by processing a number of existingdata streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users, and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regard to the components 402-48, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In some embodiments, these one or several data packets can beadaptively selected and/or selected according to one or severalselection rules. In one embodiment, for example, the recommendationengine can retrieve information from the user profile database 301identifying, for example, a skill level of the user. The recommendationengine can further retrieve information from the content librarydatabase 303 identifying, for example, potential data packets forproviding to the user and the difficulty of those data packets and/orthe skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a student-userbased on one or several received responses from that student-user. Insome embodiments, one or several data packets can be eliminated from thepool of potential data packets if the prediction indicates either toohigh a likelihood of a desired response or too low a likelihood of adesired response. In some embodiments, the recommendation engine canthen apply one or several selection criteria to the remaining potentialdata packets to select a data packet for providing to the user. Theseone or several selection criteria can be based on, for example, criteriarelating to a desired estimated time for receipt of response to the datapacket, one or several content parameters, one or several assignmentparameters, or the like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the summary model system 404 may query one or more databases and/or datastore servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include a response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices 106,supervisor devices 110, and administrator servers 116, etc. Forinstance, response system 406 may receive, aggregate, and analyze userevaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several responses into one or severalobservables can include determining whether the one or several responsesare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several responsesinto one or several observables can include characterizing the degree towhich one or several responses are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2 , and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrative servers 116, and other devices in the network 100.

With reference now to FIG. 5 , a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater).

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to the user in perceptible and/orinterpretable form, and may receive one or several inputs from the userby generating one or several electrical signals based on one or severaluser-caused interactions with the I/O subsystem such as the depressingof a key or button, the moving of a mouse, the interaction with atouchscreen or trackpad, the interaction of a sound wave with amicrophone, or the like.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that, when executed by aprocessor, provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5 , thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5 , the communications subsystem 532may include, for example, one or more location determining features 538such as one or several navigation system features and/or receivers, andthe like. Additionally and/or alternatively, the communicationssubsystem 532 may include one or more modems (telephone, satellite,cable, ISDN), synchronous or asynchronous digital subscriber line (DSL)units, FireWire® interfaces, USB® interfaces, and the like.Communications subsystem 536 also may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(e.g., using cellular telephone technology, advanced data networktechnology, such as 3G, 4G or EDGE (enhanced data rates for globalevolution), WiFi (IEEE 802.11 family standards, or other mobilecommunication technologies, or any combination thereof), globalpositioning system (GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 311). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6 , a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6 , in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6 , thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7 , a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7 , the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to a page or portal associated with the alert. In someembodiments, this launched application can display one or several itemsor pieces of data contained in the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using an other device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 8 , a schematic illustration of oneembodiment of an application stack, and particularly of a stack 650 isshown. In some embodiments, the content distribution network 100 cancomprise a portion of the stack 650 that can include an infrastructurelayer 652, a platform layer 654, an applications layer 656, and aproducts layer 658. In some embodiments, the stack 650 can comprise someor all of the layers, hardware, and/or software to provide one orseveral desired functionalities and/or productions.

As depicted in FIG. 8 , the infrastructure layer 652 can include one orseveral servers, communication networks, data stores, privacy servers,and the like. In some embodiments, the infrastructure layer can furtherinclude one or several user devices 106 and/or supervisor devices 110connected as part of the content distribution network.

The platform layer can include one or several platform softwareprograms, modules, and/or capabilities. These can include, for example,identification services, security services, and/or adaptive platformservices 660. In some embodiments, the identification services can, forexample, identify one or several users, components of the contentdistribution network 100, or the like. The security services can monitorthe content distribution network for one or several security threats,breaches, viruses, malware, or the like. The adaptive platform services660 can receive information from one or several components of thecontent distribution network 100 and can provide predictions, models,recommendations, or the like based on that received information. Thefunctionality of the adaptive platform services 660 will be discussed ingreater detail in FIGS. 9A-9C, below.

The applications layer 656 can include software or software modules uponor in which one or several product softwares or product software modulescan operate. In some embodiments, the applications layer 656 caninclude, for example, a management system, record system, or the like.In some embodiments, the management system can include, for example, aLearning Management System (LMS), a Content Management System (CMS), orthe like. The management system can be configured to control thedelivery of one or several resources to a user and/or to receive one orseveral responses from the user. In some embodiments, the records systemcan include, for example, a virtual gradebook, a virtual counselor, orthe like.

The products layer can include one or several software products and/orsoftware module products. These software products and/or software moduleproducts can provide one or several services and/or functionalities toone or several users of the software products and/or software moduleproducts.

With reference now to FIG. 9A-9C, schematic illustrations of embodimentsof communication and processing flow of modules within the contentdistribution network 100 are shown. In some embodiments, thecommunication and processing can be performed in portions of theplatform layer 654 and/or applications layer 656. FIG. 9A depicts afirst embodiment of such communications or processing that can be in theplatform layer 654 and/or applications layer 656 via the message channel412.

The platform layer 654 and/or applications layer 656 can include aplurality of modules that can be embodied in software or hardware. Insome embodiments, some or all of the modules can be embodied in hardwareand/or software at a single location, and in some embodiments, some orall of these modules can be embodied in hardware and/or software atmultiple locations. These modules can perform one or several processesincluding, for example, a presentation process 670, a response process676, a summary model process 680, and a packet selection process 684.

The presentation process 670 can, in some embodiments, include one orseveral methods and/or steps to deliver content to one or several userdevices 106 and/or supervisor devices 110. The presentation process 670can be performed by a presenter module 672 and a view module 674. Thepresenter module 672 can be a hardware or software module of the contentdistribution network 100, and specifically of the server 102. In someembodiments, the presenter module 672 can include one or severalportions, features, and/or functionalities that are located on theserver 102 and/or one or several portions, features, and/orfunctionalities that are located on the user device 106. In someembodiments, the presenter module 672 can be embodied in thepresentation system 408.

The presenter module 672 can control the providing of content to one orseveral user devices 106 and/or supervisor devices 110. Specifically,the presenter module 672 can control the generation of one or severalmessages to provide content to one or several desired user devices 106and/or supervisor devices 110. The presenter module 672 can furthercontrol the providing of these one or several messages to the desiredone or several desired user devices 106 and/or supervisor devices 110.Thus, in some embodiments, the presenter module 672 can control one orseveral features of the communications subsystem 532 to generate andsend one or several electrical signals comprising content to one orseveral user devices 106 and/or supervisor devices 110.

In some embodiments, the presenter module 672 can control and/or managea portion of the presentation functions of the presentation process 670,and can specifically manage an “outer loop” of presentation functions.As used herein, the outer loop refers to tasks relating to the trackingof a user's progress through all or a portion of a group of datapackets. In some embodiments, this can include the identification of oneor several completed data packets or nodes and/or the non-adaptiveselection of one or several next data packets or nodes according to, forexample, one or several fixed rules. Such non-adaptive selection doesnot rely on the use of predictive models, but rather on rulesidentifying next data packets based on data relating to the completionof one or several previously completed data packets or assessmentsand/or whether one or several previously completed data packets weresuccessfully completed.

In some embodiments, and due to the management of the outer loop ofpresentation functions including the non-adaptive selection of one orseveral next data packets, nodes, or tasks by the presenter module, thepresenter module can function as a recommendation engine referred toherein as a first recommendation engine or a rules-based recommendationengine. In some embodiments, the first recommendation engine can beconfigured to select a next node for a user based on one or all of: theuser's current location in the content network; potential next nodes;the user's history including the user's previous responses; and one orseveral guard conditions associated with the potential next nodes. Insome embodiments, a guard condition defines one or several prerequisitesfor entry into, or exit from, a node.

In some embodiments, the presenter module 672 can include a portionlocated on the server 102 and/or a portion located on the user device106. In some embodiments, the portion of the presenter module 672located on the server 102 can receive data packet information andprovide a subset of the received data packet information to the portionof the presenter module 672 located on the user device 106. In someembodiments, this segregation of functions and/or capabilities canprevent solution data from being located on the user device 106 and frombeing potentially accessible by the user of the user device 106.

In some embodiments, the portion of the presenter module 672 located onthe user device 106 can be further configured to receive the subset ofthe data packet information from the portion of the presenter module 672located on the server 102 and provide that subset of the data packetinformation to the view module 674. In some embodiments, the portion ofthe presenter module 672 located on the user device 106 can be furtherconfigured to receive a content request from the view module 674 and toprovide that content request to the portion of the presenter module 674located on the server 102.

The view module 674 can be a hardware or software module of some or allof the user devices 106 and/or supervisor devices 110 of the contentdistribution network 100. The view module 674 can receive one or severalelectrical signals and/or communications from the presenter module 672and can provide the content received in those one or several electricalsignals and/or communications to the user of the user device 106 and/orsupervisor device 110 via, for example, the I/O subsystem 526.

In some embodiments, the view module 674 can control and/or monitor an“inner loop” of presentation functions. As used herein, the inner looprefers to tasks relating to the tracking and/or management of a user'sprogress through a data packet. This can specifically relate to thetracking and/or management of a user's progression through one orseveral pieces of content, questions, assessments, and/or the like of adata packet. In some embodiments, this can further include the selectionof one or several next pieces of content, next questions, nextassessments, and/or the like of the data packet for presentation and/orproviding to the user of the user device 106.

In some embodiments, one or both of the presenter module 672 and theview module 674 can comprise one or several presentation engines. Insome embodiments, these one or several presentation engines can comprisedifferent capabilities and/or functions. In some embodiments, one of thepresentation engines can be configured to track the progress of a userthrough a single data packet, task, content item, or the like, and insome embodiments, one of the presentation engines can track the progressof a user through a series of data packets, tasks, content items, or thelike.

The response process 676 can comprise one or several methods and/orsteps to evaluate a response. In some embodiments, this can include, forexample, determining whether the response comprises a desired responseand/or an undesired response. In some embodiments, the response process676 can include one or several methods and/or steps to determine thecorrectness and/or incorrectness of one or several received responses.In some embodiments, this can include, for example, determining thecorrectness and/or incorrectness of a multiple choice response, atrue/false response, a short answer response, an essay response, or thelike. In some embodiments, the response processor can employ, forexample, natural language processing, semantic analysis, or the like indetermining the correctness or incorrectness of the received responses.

In some embodiments, the response process 676 can be performed by aresponse processor 678. The response processor 678 can be a hardware orsoftware module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the responseprocessor 678 can be embodied in the response system 406. In someembodiments, the response processor 678 can be communicatingly connectedto one or more of the modules of the presentation process 760 such as,for example, the presenter module 672 and/or the view module 674. Insome embodiments, the response processor 678 can be communicatinglyconnected with, for example, the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The summary model process 680 can comprise one or several methods and/orsteps to generate and/or update one or several models. In someembodiments, this can include, for example, implementing informationreceived either directly or indirectly from the response processor 678to update one or several models. In some embodiments, the summary modelprocess 680 can include the update of a model relating to one or severaluser attributes such as, for example, a user skill model, a userknowledge model, a learning style model, or the like. In someembodiments, the summary model process 680 can include the update of amodel relating to one or several content attributes including attributesrelating to a single content item and/or data packet and/or attributesrelating to a plurality of content items and/or data packets. In someembodiments, these models can relate to an attribute of the one orseveral data packets such as, for example, difficulty, discrimination,required time, or the like.

In some embodiments, the summary model process 680 can be performed bythe model engine 682. In some embodiments, the model engine 682 can be ahardware or software module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the model engine682 can be embodied in the summary model system 404.

In some embodiments, the model engine 682 can be communicatinglyconnected to one or more of the modules of the presentation process 760such as, for example, the presenter module 672 and/or the view module674, can be connected to the response processor 678 and/or therecommendation. In some embodiment, the model engine 682 can becommunicatingly connected to the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The packet selection process 684 can comprise one or several stepsand/or methods to identify and/or select a data packet for presentationto a user. In some embodiments, this data packet can comprise aplurality of data packets. In some embodiments, this data packet can beselected according to one or several models updated as part of thesummary model process 680. In some embodiments, this data packet can beselected according to one or several rules, probabilities, models, orthe like. In some embodiments, the one or several data packets can beselected by the combination of a plurality of models updated in thesummary model process 680 by the model engine 682. In some embodiments,these one or several data packets can be selected by a recommendationengine 686. The recommendation engine 686 can be a hardware or softwaremodule of the content distribution network 100, and specifically of theserver 102. In some embodiments, the recommendation engine 686 can beembodied in the packet selection system 402. In some embodiments, therecommendation engine 686 can be communicatingly connected to one ormore of the modules of the presentation process 670, the responseprocess 676, and/or the summary model process 680 either directly and/orindirectly via, for example, the message channel.

In some embodiments, and as depicted in FIG. 9A, a presenter module 672can receive a data packet for presentation to a user device 106. Thisdata packet can be received, either directly or indirectly from arecommendation engine 686. In some embodiments, for example, thepresenter module 672 can receive a data packet for providing to a userdevice 106 from the recommendation engine 686, and in some embodiments,the presenter module 672 can receive an identifier of a data packet forproviding to a user device 106 via a view module 674. This can bereceived from the recommendation engine 686 via a message channel 412.Specifically, in some embodiments, the recommendation engine 686 canprovide data to the message channel 412 indicating the identificationand/or selection of a data packet for providing to a user via a userdevice 106. In some embodiments, this data indicating the identificationand/or selection of the data packet can identify the data packet and/orcan identify the intended recipient of the data packet.

The message channel 412 can output this received data in the form of adata stream 690 which can be received by, for example, the presentermodule 672, the model engine 682, and/or the recommendation engine 686.In some embodiments, some or all of: the presenter module 672, the modelengine 682, and/or the recommendation engine 686 can be configured toparse and/or filter the data stream 690 to identify data and/or eventsrelevant to their operation. Thus, for example, the presenter module 672can be configured to parse the data stream for information and/or eventsrelevant to the operation of the presenter module 672.

In some embodiments, the presenter module 672 can extract the datapacket from the data stream 690 and/or extract data identifying the datapacket and/or indicating the selecting of a data packet from the datastream. In the event that data identifying the data packet is extractedfrom the data stream 690, the presenter module 672 can request andreceive the data packet from the database server 104, and specificallyfrom the content library database 303. In embodiments in which dataindicating the selection of a data packet is extracted from the datastream 690, the presenter module 672 can request and receiveidentification of the data packet from the recommendation engine 686 andthen request and receive the data packet from the database server 104,and specifically from the content library database 303, and in someembodiments in which data indicating the selection of a data packet isextracted from the data stream 690, the presenter module 672 can requestand receive the data packet from the recommendation engine 686.

The presenter module can then provide the data packet and/or portions ofthe data packet to the view module 674. In some embodiments, forexample, the presenter module 672 can retrieve one or several rulesand/or conditions that can be, for example, associated with the datapacket and/or stored in the database server 104. In some embodiments,these rules and/or conditions can identify portions of a data packet forproviding to the view module 674 and/or portions of a data packet to notprovide to the view module 674. In some embodiments, for example,sensitive portions of a data packet, such as, for example, solutioninformation to any questions associated with a data packet, is notprovided to the view module 674 to prevent the possibility of undesiredaccess to those sensitive portions of the data packet. Thus, in someembodiments, the one or several rules and/or conditions can identifyportions of the data packet for providing to the view module 674 and/orportions of the data packet for not providing to the view module.

In some embodiments, the presenter module 672 can, according to the oneor more rules and/or conditions, generate and transmit an electronicmessage containing all or portions of the data packet to the view module674. The view module 674 can receive these all or portions of the datapacket and can provide all or portions of this information to the userof the user device 106 associated with the view module 674 via, forexample, the I/O subsystem 526. In some embodiments, as part of theproviding of all or portions of the data packet to the user of the viewmodule 674, one or several user responses can be received by the viewmodule 674. In some embodiments, these one or several user responses canbe received via the I/O subsystem 526 of the user device 106.

After one or several user responses have been received, the view module674 can provide the one or several user responses to the responseprocessor 678. In some embodiments, these one or several responses canbe directly provided to the response processor 678, and in someembodiments, these one or several responses can be provided indirectlyto the response processor 678 via the message channel 412.

After the response processor 678 receives the one or several responses,the response processor 678 can determine whether the responses aredesired responses and/or the degree to which the received responses aredesired responses. In some embodiments, the response processor can makethis determination via, for example, use of one or several techniques,including, for example, natural language processing (NLP), semanticanalysis, or the like.

In some embodiments, the response processor can determine whether aresponse is a desired response and/or the degree to which a response isa desired response with comparative data which can be associated withthe data packet. In some embodiments, this comparative data cancomprise, for example, an indication of a desired response and/or anindication of one or several undesired responses, a response key, aresponse rubric comprising one criterion or several criteria fordetermining the degree to which a response is a desired response, or thelike. In some embodiments, the comparative data can be received as aportion of and/or associated with a data packet. In some embodiments,the comparative data can be received by the response processor 678 fromthe presenter module 672 and/or from the message channel 412. In someembodiments, the response data received from the view module 674 cancomprise data identifying the user and/or the data packet or portion ofthe data packet with which the response is associated. In someembodiments in which the response processor 678 merely receives dataidentifying the data packet and/or portion of the data packet associatedwith the one or several responses, the response processor 678 canrequest and/or receive comparative data from the database server 104,and specifically from the content library database 303 of the databaseserver 104.

After the comparative data has been received, the response processor 678determines whether the one or several responses comprise desiredresponses and/or the degree to which the one or several responsescomprise desired responses. The response processor can then provide thedata characterizing whether the one or several responses comprisedesired response and/or the degree to which the one or several responsescomprise desired responses to the message channel 412. The messagechannel can, as discussed above, include the output of the responseprocessor 678 in the data stream 690 which can be constantly output bythe message channel 412.

In some embodiments, the model engine 682 can subscribe to the datastream 690 of the message channel 412 and can thus receive the datastream 690 of the message channel 412 as indicated in FIG. 9A. The modelengine 682 can monitor the data stream 690 to identify data and/orevents relevant to the operation of the model engine. In someembodiments, the model engine 682 can monitor the data stream 690 toidentify data and/or events relevant to the determination of whether aresponse is a desired response and/or the degree to which a response isa desired response.

When a relevant event and/or relevant data are identified by the modelengine, the model engine 682 can take the identified relevant eventand/or relevant data and modify one or several models. In someembodiments, this can include updating and/or modifying one or severalmodels relevant to the user who provided the responses, updating and/ormodifying one or several models relevant to the data packet associatedwith the responses, and/or the like. In some embodiments, these modelscan be retrieved from the database server 104, and, in some embodiments,can be retrieved from the model data source 309 of the database server104.

After the models have been updated, the updated models can be stored inthe database server 104. In some embodiments, the model engine 682 cansend data indicative of the event of the completion of the model updateto the message channel 412. The message channel 412 can incorporate thisinformation into the data stream 690 which can be received by therecommendation engine 686. The recommendation engine 686 can monitor thedata stream 690 to identify data and/or events relevant to the operationof the recommendation engine 686. In some embodiments, therecommendation engine 686 can monitor the data stream 690 to identifydata and/or events relevant to the updating of one or several models bythe model engine 682.

When the recommendation engine 686 identifies information in the datastream 690 indicating the completion of the summary model process 680for models relevant to the user providing the response and/or for modelsrelevant to the data packet provided to the user, the recommendationengine 686 can identify and/or select a next data packet for providingto the user and/or to the presentation process 470. In some embodiments,this selection of the next data packet can be performed according to oneor several rules and/or conditions. After the next data packet has beenselected, the recommendation engine 686 can provide information to themodel engine 682 identifying the next selected data packet and/or to themessage channel 412 indicating the event of the selection of the nextcontent item. After the message channel 412 receives informationidentifying the selection of the next content item and/or receives thenext content item, the message channel 412 can include this informationin the data stream 690 and the process discussed with respect to FIG. 9Acan be repeated.

With reference now to FIG. 9B, a schematic illustration of a secondembodiment of communication or processing that can be in the platformlayer 654 and/or applications layer 656 via the message channel 412 isshown. In the embodiment depicted in FIG. 9B, the data packet providedto the presenter module 672 and then to the view module 674 does notinclude a prompt for a user response and/or does not result in thereceipt of a user response. As no response is received, when the datapacket is completed, nothing is provided to the response processor 678,but rather data indicating the completion of the data packet is providedfrom one of the view module 674 and/or the presenter module 672 to themessage channel 412. The data is then included in the data stream 690and is received by the model engine 682 which uses the data to updateone or several models. After the model engine 682 has updated the one orseveral models, the model engine 682 provides data indicating thecompletion of the model updates to the message channel 412. The messagechannel 412 then includes the data indicating the completion of themodel updates in the data stream 690 and the recommendation engine 686,which can subscribe to the data stream 690, can extract the dataindicating the completion of the model updates from the data stream 690.The recommendation engine 686 can then identify a next one or severaldata packets for providing to the presenter module 672, and therecommendation engine 686 can then, either directly or indirectly,provide the next one or several data packets to the presenter module672.

In some embodiments, of the communication as shown in FIGS. 9A and 9B,all communications between any of the presenter module 672, the responseprocessor 678, the model engine 682, and the recommendation engine 686can pass through the message channel 412. Alternatively, in someembodiments, some of the communications between any of the presentermodule 672, the response processor 678, the model engine 682, and therecommendation engine 686 can pass through the message channel andothers of the communications between any of the presenter module 672,the response processor 678, the model engine 682, and the recommendationengine 686 can be direct.

With reference now to FIG. 9C, a schematic illustration of an embodimentof dual communication, or hybrid communication, in the platform layer654 and/or applications layer 656 is shown. Specifically, in thisembodiment, some communication is synchronous with the completion of oneor several tasks and some communication is asynchronous. In theembodiment depicted in FIG. 9C, the presenter module 972 communicatessynchronously with the model engine 682 via a direct communication 692and communicates asynchronously with the model engine 682 via themessage channel 412.

In some embodiments, and as depicted in FIG. 9C, the synchronouscommunication and/or the operation of the presenter module 672, theresponse processor 678, the model engine 682, and the recommendationengine 686 can be directed and/or controlled by a controller. In someembodiments, this controller can be part of the server 102 and/orlocated in any one or more of the presenter module 672, the responseprocessor 678, the model engine 682, and the recommendation engine 686.In some embodiments, this controller can be located in the presentermodule 672, which presenter module can control communications with andbetween itself and the response processor 678, the model engine 682, andthe recommendation engine 686, and the presenter module can thus controlthe functioning of the response processor 678, the model engine 682, andthe recommendation engine 686.

Specifically, and with reference to FIG. 9C, the presenter module 672can receive and/or select a data packet for presentation to the userdevice 106 via the view module 674. In some embodiments, the presentermodule 672 can identify all or portions of the data packet that can beprovided to the view module 674 and portions of the data packet forretaining from the view module 674. In some embodiments, the presentermodule can provide all or portions of the data packet to the view module674. In some embodiments, and in response to the receipt of all orportions of the data packet, the view module 674 can provide aconfirmation of receipt of the all or portions of the data packet andcan provide those all or portions of the data packet to the user via theuser device 106. In some embodiments, the view module 674 can providethose all or portions of the data packet to the user device 106 whilecontrolling the inner loop of the presentation of the data packet to theuser via the user device 106.

After those all or portions of the data packet have been provided to theuser device 106, a response indicative of the completion of one orseveral tasks associated with the data packet can be received by theview module 674 from the user device 106, and specifically from the I/Osubsystem 526 of the user device 106. In response to this receive, theview module 674 can provide an indication of this completion status tothe presenter module 672 and/or can provide the response to the responseprocessor 678.

After the response has been received by the response processor 678, theresponse processor 678 can determine whether the received response is adesired response. In some embodiments, this can include, for example,determining whether the response comprises a correct answer and/or thedegree to which the response comprises a correct answer.

After the response processor has determined whether the receivedresponse is a desired response, the response processor 678 can providean indicator of the result of the determination of whether the receivedresponse is a desired response to the presenter module 672. In responseto the receipt of the indicator of whether the result of thedetermination of whether the received response is a desired response,the presenter module 672 can synchronously communicate with the modelengine 682 via a direct communication 692 and can asynchronouslycommunicate with model engine 682 via the message channel 412. In someembodiments, the synchronous communication can advantageously includetwo-way communication between the model engine 682 and the presentermodule 672 such that the model engine 682 can provide an indication tothe presenter module 672 when model updating is completed by the modelengine.

After the model engine 682 has received one or both of the synchronousand asynchronous communications, the model engine 682 can update one orseveral models relating to, for example, the user, the data packet, orthe like. After the model engine 682 has completed the updating of theone or several models, the model engine 682 can send a communication tothe presenter module 672 indicating the completion of the updated one orseveral modules.

After the presenter module 672 receives the communication indicating thecompletion of the updating of the one or several models, the presentermodule 672 can send a communication to the recommendation engine 686requesting identification of a next data packet. As discussed above, therecommendation engine 686 can then retrieve the updated model andretrieve the user information. With the updated models and the userinformation, the recommendation engine can identify a next data packetfor providing to the user, and can provide the data packet to thepresenter module 672. In some embodiments, the recommendation engine 686can further provide an indication of the next data packet to the modelengine 682, which can use this information relating to the next datapacket to update one or several models, either immediately, or afterreceiving a communication from the presenter module 672 subsequent tothe determination of whether a received response for that data packet isa desired response.

With reference now to FIG. 9D, a schematic illustration of oneembodiment of the presentation process 670 is shown. Specifically, FIG.9D depicts multiple portions of the presenter module 672, namely, theexternal portion 673 and the internal portion 675. In some embodiment,the external portion 673 of the presenter module 672 can be located inthe server, and in some embodiments, the internal portion 675 of thepresenter module 672 can be located in the user device 106. In someembodiments, the external portion 673 of the presenter module can beconfigured to communicate and/or exchange data with the internal portion675 of the presenter module 672 as discussed herein. In someembodiments, for example, the external portion 673 of the presentermodule 672 can receive a data packet and can parse the data packet intoportions for providing to the internal portion 675 of the presentermodule 672 and portions for not providing to the internal portion 675 ofthe presenter module 672. In some embodiments, the external portion 673of the presenter module 672 can receive a request for additional dataand/or an additional data packet from the internal portion 675 of thepresenter module 672. In such an embodiments, the external portion 673of the presenter module 672 can identify and retrieve the requested dataand/or the additional data packet from, for example, the database server104 and more specifically from the content library database 104.

With reference now to FIG. 10A, a flowchart illustrating one embodimentof a process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the presentation system 408 and/or by thepresentation module or presentation engine. In some embodiments, theprocess 440 can be performed as part of the presentation process 670.

The process 440 begins at block 442, wherein a data packet isidentified. In some embodiments, the data packet can be a data packetfor providing to a student-user. In some embodiments, the data packetcan be identified based on a communication received either directly orindirectly from the recommendation engine 686.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the presentation system408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to thestudent user, which content can include one or several requests and/orquestions and/or the like. In some embodiments, one of these datacomponents, referred to herein as a response component, can include dataused in evaluating one or several responses received from the userdevice 106 in response to the data packet, and specifically in responseto the presentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as thestudent-user via the user device 106. In some embodiments, the deliverypacket can include the presentation component, and in some embodiments,the delivery packet can exclude the response packet. After the deliverydata packet has been generated, the process 440 proceeds to block 452,wherein the delivery data packet is provided to the user device 106 andmore specifically to the view module 674. In some embodiments, this caninclude providing the delivery data packet to the user device 106 via,for example, the communication network 120.

After the delivery data packet has been provided to the user device 106,the process 440 proceeds to block 454, wherein the data packet and/orone or several components thereof are sent to and/or provided to theresponse processor 678. In some embodiments, this sending of the datapacket and/or one or several components thereof to the responseprocessor can include receiving a response from the student-user, andsending the response to the student-user to the response processorsimultaneous with the sending of the data packet and/or one or severalcomponents thereof to the response processor. In some embodiments, forexample, this can include providing the response component to theresponse processor. In some embodiments, the response component can beprovided to the response processor from the presentation system 408.

With reference now to FIG. 10B, a flowchart illustrating one embodimentof a process 460 for evaluating a response is shown. In someembodiments, the process can be performed as a part of the responseprocess 676 and can be performed by, for example, the response system406 and/or by the response processor 678. In some embodiments, theprocess 460 can be performed by the response system 406 in response tothe receipt of a response, either directly or indirectly, from the userdevice 106 or from the view module 674.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

) After the response type has been identified, the process 460 proceedsto block 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the receive response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments, this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more aplurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

With reference to the FIG. 11 , a flowchart illustrating one embodimentof a process 1100 for automatic difficulty determination of the datapacket and placement of that data packet in the content network isshown. The process 1100 can be performed by all or components of thecontent distribution network 100 including by, for example, one orseveral servers 102. In some embodiments, these one or several servers102 can comprise one or several remote resources such as can occur viacloud computing or distributed processing. The process 1100 begins atblock 1102 when a data packet is received and/or identified. In someembodiments, the data packet can be a data packet for which responseshave not been received. In some embodiments, this data packet does notinclude data metadata identifying one or several attributes of the datapacket such as, for example, data packet difficulty level,discrimination level, or the like. In some embodiments, the data packetcan be received by the server 102 from another component of the contentdistribution network 100 such as, for example, the supervisor device 110or content server 112. In some embodiments, the data packet can beidentified by the server 102 from one or several data packets stored inthe database server 104 such as in, for example, the content librarydatabase 303 of the database server 104.

After the data packet has been received and/or identified, the process1100 proceeds to block 1104 wherein data packet information, alsoreferred to herein as data packet metadata, is retrieved and/orreceived. In some embodiments, the data packet metadata can identify oneor several attributes of the data packet such as, for example, thedifficulty level of all or portions of the data packet, thediscrimination level of all or portions of the data packet, or the like.In some embodiments, the data packet metadata can be received with thedata packet in block 1102, and in some embodiments, the data packetmetadata can be retrieved from the database server 104 and specificallyfrom, for example, the content library database 303 of the databaseserver 104.

After the data packet information has been retrieved, the process 1100proceeds to block 1106 wherein it is determined if the data packetmetadata includes data identifying a difficulty level and/ordiscrimination level of the data packet. In some embodiments, this caninclude analyzing the data packet metadata in determining the presenceor absence of portions the data packet metadata identifying a difficultylevel and/or discrimination level of the data packet. In someembodiments, this determination can be made by the server 102. If it isdetermined that the data packet metadata contains data packet difficultyinformation and/or data packet discrimination information, then theprocess 1100 proceeds to block 1107 and continues to block 1206 of FIG.12 with the data packet received in block 1102 and continues asdescribed with respect to FIG. 12 below.

The process 1100 continues to decision state 1108 wherein it isdetermined if any additional data packets have been received orretrieved. If no additional data packets have been received orretrieved, then the process proceeds to block 1109 and waits until a newdata packet has been received. After the new data packet has beenreceived, the process 1100 returns to block 1104 and procedures outlinedherein. Returning again to decision state 1108, if it is determined thatthere is an additional data packet, then the process 1100 returns toblock 1104 and procedures outlined herein.

Returning again to decision state 1106, if it is determined thedifficulty level of the data packet received in block 1102 has not beendetermined, then the process 1100 proceeds to block 1110 wherein thedata packet content network of the received data packet is identified.In some embodiments, for example, the data packet content network can beidentified based on information contained in the data packet metadataand information relating to the content network which informationrelating to the content network is referred to herein as content networkmetadata. In some embodiments, the information contained in the datapacket metadata can identify a subject of the data packet and/or asubject to which the data packet belongs. Similarly, in someembodiments, the content network metadata can identify a subject of thecontent network and/or tasks, skills, objectives, topics, or the likebelonging to the content network. In some embodiments, the informationcontained in the data packet metadata and in the content networkmetadata can be retrieved and/or extracted. The information contained inthe data packet metadata can then be matched to the informationcontained in content network metadata to identify the content network towhich the data packet belongs. This content network to which the datapacket belongs is also referred to herein as the data packet contentnetwork. In some embodiments, the identification of the data packetcontent network can be performed by the server 102 and/or any of themodules are engines of the server 102 or other portion of the contentdistribution network 100.

After the data packet content network has been identified, the process1100 proceeds to block 1112 wherein a position in the content network isidentified and/or determined for insertion of the data packet receivedin block 1102. In some embodiments, the position of the content networkcan be identified based on the data packet metadata and/or the contentnetwork metadata. In some embodiments, the position of the contentnetwork can be identified and/or selected such that the data packet isplaced in the content network with other data packets having similarsubjects, topics, skill levels, or the like. This identification can beperformed by the server 102 or any other component of the contentdistribution network 100.

After the position of the content network has been identified, theprocess 1100 proceeds to block 1114 wherein similarly positioned datapackets are identified. In some embodiments, this identification can beperformed by retrieving data packets already included in the contentnetwork and having subjects, topics, skills, skill levels, or the likeidentified in their metadata that correspond to those subjects, topics,skills, skill levels, or the like identified in the data packet receivedin block 1102. In some embodiments, the metadata for the data packetsalready positioned in the content network can be retrieved from thedatabase server 104 by the server 102, and can be specifically retrievedfrom the content library database server 303 by the server 102.

After similarly positioned data packets have been identified, theprocess 1100 proceeds block 1116 wherein attribute information for thoseone or several similarly positioned data packets is retrieved. In someembodiments, this attribute information can identify, for example,difficulty levels of the similarly positioned data packets,discrimination levels of the similarly positioned data packets, or thelike. In some embodiments, this attribute information can be retrievedby retrieving the metadata from the database server 104 for thesimilarly positioned data packets and extracting the desired attributeinformation from the retrieved metadata. In some embodiments, thesimilarly positioned data packets for which the metadata is retrievedand for which the attribute information is determined or identified canbe the similarly positioned data packets identified in block 1114.

After the attribute information for the similarly positioned datapackets has been received and/or retrieved, the process 1100 proceeds toblock 1118 wherein a combined attribute value is generated. In someembodiments, this combined attribute value reflects the aggregate of theattribute information retrieved in block 1116. In some embodiments, thiscombined value can comprise a mean, median, mode, or any othermeaningful combined value. In some embodiments, the combined attributevalue can comprise an average difficulty level, an averagediscrimination level, or the like. The combined attribute value can be,in some embodiments, generated by the server 102 or any other desiredcomponent of the content distribution network 100.

After the combined attribute value has been determined, the process 1100proceeds to block 1120 wherein the value of the attribute of the datapacket received in block 1102 corresponding to the combined attributevalue is set to match the combined attribute value. This can include,for example, setting the difficulty level of the data packet received inblock 1102 to the average difficulty level generated in block 1118 forsimilarly positioned data packets identified in block 1114 when thecombined attribute value comprises an average difficulty level.Similarly, this can include setting the discrimination level of the datapacket received in block 1102 to the average discrimination levelgenerated in block 1118 for the similarly positioned data packetsidentified in block 1114 when the combined attribute value comprises anaverage discrimination level. In some embodiments, the value of theattribute of the data packet received in block 1102 can be set to theaverage attribute value generated in block 1118 by the server 102 and anindicator of this can be stored in the database server 104 andspecifically in the content library database 303. In other words, insome embodiments, the metadata of the data packet received in block 1102can be updated to reflect an attribute value corresponding to thecombined attribute value generated in block 1118. In such an embodiment,the metadata of the data packet received in block 1102 can be updated tohave a difficulty level matching the average difficulty level generatedin block 1118. After the received packet attribute value has been set tothe combined attribute value, the process 1100 returns to block 1107 andproceeds as outlined above.

With reference now to FIG. 12 , a flowchart illustrating one embodimentof a process 1200 for data packet metadata stabilization is shown. Theprocess 1200 can be performed by all or components of the contentdistribution network 100 including by, for example, one or severalservers 102. In some embodiments, these one or several servers 102 cancomprise one or several remote resources such as can occur via cloudcomputing or distributed processing. The process 1200 begins to block1202 wherein the data packet is received and/or identified. In someembodiments, the data packet can be received from, for example, block1107 of FIG. 11 . In some embodiments, the data packet can be identifiedfrom one of the data packet stored in the database 104 specifically inthe content library database 303.

After the data packet has been received or identified, process 1200proceeds to block 1204 wherein data packet information, also referred toherein as data packet metadata is retrieved. In some embodiments, datapacket information can be retrieved for the data packet received and/oridentified in block 1202. In some embodiments, the data packet metadatacan identify one or several attributes of the data packet such as, forexample, the difficulty level of all or portions of the data packet, thediscrimination level of all or portions of the data packet, or the like.In some embodiments, the data packet metadata can be received with thedata packet in block 1202, and in some embodiments, the data packetmetadata can be retrieved from the database server 104 and specificallyfrom, for example, the content library database 303 of the databaseserver 104.

After the data packet metadata has been retrieved, the process 1200proceeds to decision state 1206 where it is determined if one or severalattributes identified in the data packet metadata are stable. In someembodiments, for example, the one or several attributes can beidentified in the data packet data by one or several values, alsoreferred to herein as attribute values. In some embodiments, forexample, one or several of these attribute values can identify thedifficulty and/or discrimination of the data packet received and/oridentified in block 1202.

In some embodiments, the data packet metadata can include time seriesdata tracking the one or several attribute values over time. In someembodiments, decision state 1206 can include determining whether the oneor several attribute values are stable as indicated by their time seriesdata. Alternatively, in some embodiments, the determination of thestability of the attribute values can comprise determining the number ofresponses received for the data packet received and/or identified inblock 1202 and retrieving a threshold, referred to herein as a stabilitythreshold, from the database server 104 and specifically from thethreshold database 310 which delineates between stable attribute valuesand unstable attribute values based on, for example, the number ofresponses received foreign attribute value. In some embodiments, forexample, this threshold can identify a minimum number of responses to bereceived before an attribute value is identified as stable. In someembodiments, this minimum number of responses can be, for example, 100responses, 200 responses, 500 responses, 750 responses, 1000 responses,2000 responses, 5000 responses, 10,000 responses, 50,000 responses,100,000 responses, and/or any other or intermediate number of responses.In some embodiments, the determined number of responses received for thedata packet received and/or identified in a block 1102 can be comparedto the stability threshold to determine whether the required minimumnumber of responses has been received for the data packet identified inblock 1202.

If it is determined that the desired number of responses has beenreceived, the process 1200 proceeds to block 1208 wherein a stabilityindicator is added to the data packet metadata associated with the datapacket received and/or identified in block 1202 and/or wherein astability indicator is stored within the database server 104 inassociation with the data packet retrieved and/or identified in block1202. In some embodiments, the stability indicator can comprise one orseveral values, one of which values can be indicative of the achievementof stability for the attribute values of the data packet received inblock 1202. In some embodiments, the adding of a stability indicator canfurther comprise the generating and sending of an alert to a device suchas the supervisor device 110. In some embodiments, this alert canidentify the data packet and that the difficulty of the data packet isstable. In some embodiments, the alert can contain other informationrelevant to the data packet received and/or identified in block 1202.

Returning again to decision state 1206, if it is determined that thedifficulty level is not stable, then the process 1200 proceeds to block1210 wherein one or several potential packet recipients are identified.In some embodiments, the one or several potential packet recipients canbe, for example, one or several users belonging to a common cohort. Insome embodiments, this common cohort can include registration in one orseveral groups, classes, courses of study, disciplines, or the like. Insome embodiments, identifying the potential packet recipients caninclude identifying one or several cohorts related to the data packetreceived and/or identified in block 1202 based on, for example, datapacket metadata for the data packet received in block 1202. In someembodiments, the database server 104 can then be queried for informationidentifying one or several users belonging to these one or severalcohorts. In some embodiments, this query can be directed to user profiledatabase 301. The potential packet recipients can, in some embodiments,be identified by the server 102.

In some embodiments, the set of potential packet recipients can compriseusers requesting a next data packet or an additional data packet. Insome embodiments, the set of potential packet recipients can be formallyidentified in that some potential packet recipients are designated asbelonging to this set of potential packet recipients and/or in that thisset of potential packet recipients exists in, for example, the databaseserver 104. In some embodiments, this set of potential packet recipientscan merely comprise the group of users requesting a next packet, andspecifically requesting a next packet that can include the data packetidentified in block 1202.

After the potential packet recipients have been identified, the process1200 proceeds to block 1212, wherein user data for the potential packetrecipients is retrieved and/or received. In some embodiments, this userdata can comprise the metadata for each of the users in the group ofpotential packet. This metadata can be received from the database server104 and specifically from the user profile database 301 by the server102 upon query of the user profile database 301 for that metadata by theserver 102.

After the user data has been retrieved, the process 1200 proceeds toblock 1214 wherein a data packet attribute value is identified. In someembodiments, the data packet attribute value can be one or severalvalues that can characterize a difficulty level of the data packetreceived in block 1202 and/or that can characterize the discriminationlevel of the data packet received in block 1202. In some embodiments,the data packet metadata can be retrieved in the database server 104 andspecifically from the content library database 303. In some embodiments,as the data packet metadata has already been retrieved in block 1204,block 1214 can include extracting one or several attribute values fromthat metadata.

After the data packet attribute value has been identified, the process1200 proceeds block 1216 wherein a user attribute value is identified.In some embodiments, the user attribute value can be one or severalvalues that characterize, for example, the skill level of the user, alearning style of the user, or the like. In some embodiments, the userattribute value can be identified from user metadata that can beretrieved from the database server 104 and specifically from the userprofile database 301.

After the skill level has been identified, the process 1200 proceeds toblock 1218 wherein a recipient of the data packet is selected. In someembodiments, the recipient is the user who will receive the data packetreceived and/or identified in block 1202. In some embodiments, therecipient of the data packet can be selected based on, for example,comparison of one or several data packet attribute values in one orseveral user attribute values. The recipient of the data packet can beselected by the server 102.

After the recipient of the packet has been selected, the process 1200proceeds to block 1220 wherein a non-impact value is associated with therecipient. In some embodiments, the non-impact value identifies the datapacket received in block 1202 and selected for delivery to the recipientselected in block 1218 as excluded from calculations of user attributevalues. In some embodiments, for example, because instability of theattribute values of the data packet as determined in decision state1206, modifications to the user metadata with results of a user responserelating to the data packet would be unreliable. Accordingly, thenon-impact value is associated with the recipient and the data packet tobe provided to the recipient so that the recipient's user metadata isnot modified based on the result of any response provided by therecipient. In some embodiments, the non-impact value can be associatedwith the recipient by storing the non-impact value in the databaseserver 104 and specifically in the user profile database 301. In someembodiments, the non-impact value can be further associated with thedata packet received in block 1202 by storing the non-impact value inthe content library database 303 and linked to the data packet receivedin block 1202.

After the nonimpact value has been associated with the recipient, theprocess 1200 proceeds to block 1222 wherein the data packet is providedto the recipient. In some embodiments, the data packet can be providedto the recipient via the user device 106, and specifically by providingthe data packet to the presenter module 672 which can then provide thedata packet to the view module 674 which can use the I/O subsystem 526to provide the data packet and/or portions of the data packet to theuser of the user device 106.

After the data packet has been provided, the process 1200 proceeds block1224 wherein the responses received. In some embodiments, the responsesreceived from the user by the server 102. Specifically, the user canprovide a response to the data packet provided in block 1222 at the userdevice 106 via the I/O subsystem 526 which can pass the response to theview module 674. To view module can then provide the response thepresenter module 672, and bus the response can be received by the server102.

After the responses have been received, the process 1200 proceeds toblock 1226 wherein the responses are translated into an observable. Insome embodiments, the response translated into an observable is theresponse received in block 1224, which response is provided by therecipient subsequent to receiving the data packet. In some embodiments,translating the received response into an observable includes evaluatingthe received response to determine if the received response includes oneor several attributes, or alternatively evaluating the received responseto determine if the received response is a desired response. In someembodiments, the response can be translated into an observable by theresponse system 406 and/or the response processor 678.

After the response has been translated into an observable, the process1200 proceeds to block 1228, wherein the data packet metadata and/ordata packet model is updated. In some embodiments, the data packetmetadata and/or data packet model associated with the data packetprovided in block 1222 can be updated based on the observable generatedin block 1226 via the translation of the response received in block1224. In some embodiments, this can include updating one or several datapacket attribute values contained in the data packet metadata, andspecifically can include updating a difficulty level of the data packetaccording to whether the response received in block 1224 was correct orincorrect. In some embodiments, the data packet metadata and/or datapacket can be updated by the summary model system 404 and/or the modelengine 682. The details of the updating of the data packet metadataand/or data packet model will be discussed in greater detail withrespect to the process 1400 of FIG. 14 below. After the data packetmetadata and/or data packet model has been updated, the process 1200returns to decision state 1206 and proceeds as outlined above.

With reference to FIG. 13 , a flowchart illustrating one embodiment of aprocess 1300 for selecting recipient of the data packet shown. In someembodiments, the process 1300 can be performed in the place of as a partof block 1218 of FIG. 12 . The process 1300 can be performed by anycomponent of the content distribution network 100 including, forexample, the server 102. The process begins at block 1302 wherein asubset of users is selected. In some embodiments, the subset of usersselected is based on only portions of the data packet metadata of thedata packet received in block 1202 and/or the user metadata. In someembodiments, the subset of users can be selected from the potentialpacket recipients identified in block 1210 of FIG. 12 . In someembodiments, the subset of users can be selected by identifying one orseveral users within the group of potential packet recipients having askill level corresponding to and/or matching the difficulty level and/orthe discrimination level of the data packet received in block 1202.

After the subset of users has been selected, the process 1300 proceedsto block 1304 wherein one of the subset of users is selected foranalysis in some embodiments, the selected one of the subset of userscan be the user that has not been previously selected for analysis. Insome embodiments, the selecting one user for analysis can includeidentifying the users in the subset of users that have not beenpreviously selected for analysis and then selecting one of thoseidentified users. In some embodiments, when a user is selected foranalysis, a value indicative of selection can be associated with thatuser. The one of the subset users can, in some embodiments, be selectedby the server 102.

After one of the subset of users has been selected for analysis, process1300 proceeds to block 1306 wherein user load levels of the selecteduser are determined. In some embodiments, the user load levels cancomprise data that can be contained in the user metadata and that canidentify, for example, the amount of time, or the number of data packetsfor a user to complete a task relating to the data packet received inblock 1202. In some embodiments, determining the user load levels cancomprise extracting this information from the user metadata with, forexample, the server 102. After the user load levels have beendetermined, the process 1300 proceeds to block 1308 wherein a loadthreshold is retrieved. In some embodiments, below threshold delineatesbetween acceptable load levels and unacceptable load levels. The lowthreshold can be stored in the database server 104, and specifically inthe threshold database 310. In some embodiments, the load threshold canbe generic. In some embodiments the load threshold can be specific to,for example, one or several data packets, courses, tasks, or the like.In some embodiments, the load threshold retrieved in block 1308 can berelevant to the data packet received in block 1202.

After the load threshold has been retrieved, the process 1300 proceedsto block 1310 wherein a predicted load value is generated. In someembodiments, the predicted load value can comprise the load leveldetermined in block 1306 adjusted to reflect inclusion of the datapacket received in block 1202. In some embodiments, this can measureand/or predict the user load level if the data packet received in block1202 is provided to the user, or in other words, if that user isselected as the recipient. In some embodiments, the predicted load valuecan be generated by the server 102.

After the predicted load value has been generated, the process 1300proceeds to block 1312 when the predicted load value is compared to theload threshold. In some embodiments, this comparison can includeassociating a first value with the user selected in block 1304 if thecomparison of the load value and the load threshold indicates anacceptable load level and the second value can be associated with theuser selected in block 1304 if the comparison of the load value and theload threshold indicates an unacceptable load level. In someembodiments, the predicted load value can be compared to the loadthreshold by the server 102.

After the predicted load value has been compared to the load threshold,the process 1300 proceeds to decision state 1314 wherein it isdetermined if the load level is acceptable. In some embodiments, thiscan include determining if the first value or the second value isassociated with the user selected in block 1304. If the second value isassociated with the user selected in block 1304 and the load level isthus unacceptable, then the process 1300 proceeds to block 1316 and theuser selected in block 1304 is removed from the set of potential packetrecipients.

After the user has been removed from the set of potential packetrecipients, returning again to decision state 1314, if it is determinedthat the load level is acceptable, then the process 1300 proceeds todecision state 1320 wherein it is determined if there are additionalusers. In some embodiments, this can include determining if there areadditional, unselected users in the subset of users selected in block1302. If it is determined that there are additional, unselected users,then the process 1300 returns to decision state 1304 and proceeds asoutlined above.

If it is determined there no additional users, than the process 1300proceeds to block 1322 wherein a recipient is designated. In someembodiments, this can include determining whether any of the users inthe set of potential users is associated with a first value indicativeof an acceptable load level. If only a single user is associated withthe first value indicative of an acceptable load level, then that singleuser is designated as the recipient by the server 102. If multiple usersare associated with the first value indicative of an acceptable votelevel, then one of those multiple users can be designated as therecipient. In some embodiments, for example, this one of the multipleusers can be randomly selected from the multiple users that areassociated with the first value by, for example the server, and in someembodiments, this one of the multiple users can be selected based on thecloseness of the match between the attribute values of the data packetreceived in block 1202 and attribute values of the users, the degree towhich users' predicted load levels are acceptable, or the like. Afterthe recipient has been designated, the process 1300 proceeds to block1324 and continues at block 1220 of FIG. 12 .

With reference now to FIG. 14 , a flowchart illustrating one embodimentof a process 1400 for automatically updating data packet metadata and/orautomatically updating a data packet model is shown. In someembodiments, the process 1400 can be performed as a part of or in placeof the step of block 1228 of FIG. 12 , and in some embodiments, theprocess 1400 can be performed independent of the process of FIG. 12 .The process 1400 can be performed by the server 102, and specificallyby, for example, the response system 406, the response processor 678,the summary model system 404 and/or the model engine 682. The process1400 begins at block 1402 wherein a response is received. In someembodiments, the response can be received from a user device 106 via theI/O subsystem 526, the communication subsystem 532, the communicationsnetwork 102, the view module 674, and/or the presenter module 672. Insome embodiments, the response can be associated with a previouslyprovided data packet that can be selected for the user based on one orseveral of the user metadata, the data packet metadata, or the like.

After the responses have been received, the process 1400 proceeds toblock 1404 wherein the data packet associated with the receivedresponses is identified. In some embodiments, for example, the responsecan include information relating to the data packet prompting theresponse. In some embodiments, for example, this information can beextracted from the response and can be used to identify the data packetprovided to the user previous to the response and giving rise to theresponse. In some embodiments, the data packet can be identified by theserver 102.

After the data packet has been identified, the process 1400 proceeds toblock 1406 wherein the data packet metadata is retrieved. In someembodiments, the data packet metadata can comprise information relatingto one or several attributes of the data packet. In some embodiments,the data packet metadata can be associated with the data packetidentified in block 1404 and the data packet metadata can be retrievedfrom the database server 104 and specifically from the content librarydatabase 303.

In some embodiments, the one or several attributes can be representedand/or determined through one or several models, such as statisticalmodels. In some embodiments, these models can be continuously orperiodically updated by the content distribution network 100 when one orseveral new responses are received. In some embodiments, these modelscan be stored in, for example, the user profile database 301, the modeldatabase 309, or any of the other databases 104. In some embodiments, amodel can be associated with a single objective such that the model isnot applicable to other objectives, and in some embodiments, the modelcan be associated with a plurality of objectives.

In some embodiments, a user skill level and/or a data packet difficultylevel can be determined via a model based on, for example, a Gaussiandistribution. In some embodiments, the data packet difficulty can bedetermined from the mode of a Gaussian distribution and/or a piece-wiseGaussian distribution, and a user skill level can likewise be determinedfrom the mode of a Gaussian distribution and/or a piece-wise Gaussiandistribution.

One example of a piece-wise Gaussian distribution 1500 is shown in FIG.15 . As seen in FIG. 9 , the piece-wise Gaussian distribution 1500 hasan x-axis labelled “x” and a y-axis labelled “density.” As depicted inFIG. 15 , the units of the x-axis indicate a skill level of the user(s)associated with the piecewise Gaussian distribution 1500, with the skilllevel increasing in a positive correlation with the values of thex-axis. The units of the y-axis, density, indicate a density thatdescribes the likelihood of a random variable selected according to thepiecewise Gaussian distribution 1500 to take on a value on the x-axis.

The piecewise Gaussian distribution 1500 includes a curve 1502 that hasa first piece 1504 forming the right side of the piecewise Gaussiandistribution 1500, and a second piece 1506 forming the left side of thepiecewise Gaussian distribution 1500. In some embodiments, the mode ofthe piecewise Gaussian distribution 1500 can represent the skill levelof the user.

In some embodiments, the property of the Gaussian distribution, such as,for example, the width of the Gaussian distribution or of the piecewiseGaussian distribution can represent or positively correlate to the levelof certainty, and/or can be used to determine an error value. In someembodiments, the level of uncertainty and/or an error value can bemathematically calculated based on one or several variables and/orparameters tied to the Gaussian distribution and/or the piecewiseGaussian distribution 1500.

In some embodiments, the piecewise Gaussian distribution 1500 canfurther include a first level of certainty and/or first error valueassociated with the first piece 1504 of the piecewise Gaussiandistribution 1500, and a second level of certainty and/or a second errorvalue associated with the second piece 1506 of the piecewise Gaussiandistribution 1500. In such embodiments, the width of the Gaussiandistribution and/or piecewise Gaussian distribution 1500, and/or thewidth of one or both of the pieces 1504, 1506 of the piecewise Gaussiandistribution 1500 can vary in a positive relation with the level ofuncertainty and/or with the error value. Thus, as the width decreases,the level of uncertainty and/or the error value decreases, and as thewidth increases, the level of uncertainty and/or the error valueincreases.

In some embodiments, the first piece 1504 and the second piece 1506 canhave different properties to represent different traits of one orseveral users. Specifically, and as seen in FIG. 15 , the tail of thefirst piece 1504 of the piecewise Gaussian distribution 1500 extendfarther than the tail of the second piece 1506 of the piecewise Gaussiandistribution 1500. In some embodiments, this serves to provide asemi-ratcheting effect to changes to the piecewise Gaussian distribution1500 when either desired responses or undesired responses are received.Specifically, this semi-ratcheting effect results in a user's skilllevel more quickly increasing when a desired response is received thandecreasing when an undesired response is received. This can, in someembodiments, correlate to the user property of accepting data, or morespecifically, gaining knowledge faster than losing knowledge.

This semi-ratcheting effect is illustrated in FIG. 16 which depicts aplurality of piecewise Gaussian distributions 1520. The plurality ofpiecewise Gaussian distributions 1520 includes a prior distribution1522, a correct distribution 1524, which is the Gaussian distributionresulting from receipt of a desired response to an assessment datapacket, and an incorrect distribution 1526, which is the piecewiseGaussian distribution resulting from receipt of an undesired response toan assessment data packet.

As depicted in FIG. 16 , the prior response has a mode of −0.2, and thusdepicts a user skill level of −0.2. This skill level shifts, in responseto the received desired response to 2.2, and shifts, in response to thereceived undesired response of −1.5. Thus, the semi-ratcheting effect ofthe piecewise Gaussian distribution is that the skill level increase atotal of 2.5 as a result of the received desired response and onlydecreases a total of 1.3 as a result of the received undesired response.As further seen, each of the correct and incorrect distributions 1524,1526 is narrower than the prior distribution 1522, indicating lowerlevels of uncertainty and greater levels of certainty associated withthose models. In some embodiments, the degree to which the width of thepiece-wise Gaussian distribution 1500 can shrink can be limited. In someembodiments, for example, the first piece 1504 of the piece-wiseGaussian distribution 1500 can be prohibited from having a scale factorsmaller than, for example, approximately: 3.0; 2.5; 2.0; 1.75; 1.5;1.25; 1.0; 0.75; 0.5; 0.25; and/or any other or intermediate value, andin some embodiments, for example, the second piece 1506 of thepiece-wise Gaussian distribution 1500 can be prohibited from having ascale factor smaller than, for example, approximately: 3.0; 2.5; 2.0;1.75; 1.5; 1.25; 1.0; 0.75; 0.5; 0.25; 0.15; 0.1; 0.05; and/or any otheror intermediate value. As used herein, “approximately” identifies arange about the therewith associated value, which range is +/−25%, 20%,15%, 10%, 5%, and/or any other or intermediate percent of that therewithassociated value.

After the data packet metadata has been retrieved, the process 1400proceeds to block 1408 wherein the response data is evaluated. In someembodiments, this can include the translation of the response into anobservable by, for example, the response processor 678. In someembodiments, this translation of the response into an observable caninclude determining whether the response is a desired response or anundesired response. Evaluation the response can be performed by theresponse processor 678 based on answer data associated with the datapacket and, in some embodiments, stored in the content library database303.

After the response data has been evaluated, the process 1400 proceeds toblock 1410 wherein one or several attribute values of the data packetfor which the responses received in block 1402 are updated. In someembodiments, this can include updating the user attributes to reflect anew skill level and/or new confidence level or error value, and in someembodiments, this can include updating the objective and/or data packetdata to reflect a new difficulty level and/or new confidence level orerror value. In some embodiments, the date of attributes in block 1410can include the updating of both user attributes and data packetattributes unless the user associated with the response received inblock 1402 is associated with a non-impact value as can be generated inblock 1220 of FIG. 12 .

In some embodiments, the user data can be updated to reflect the desiredor undesired response received. In some embodiments in which the userprovides an undesired response, the user skill level can stay the sameand/or be negatively changed, and in some embodiments in which the userprovides the desired response, the user skill level can positivelychange. In some embodiments, the user skill level can be changedaccording to one or several predictive models which can be, for example,one or several probabilistic models.

In some embodiments in which one or several user attributes and/orobjective or data packet attributes are modeled according to a Gaussiandistribution and/or a piecewise Gaussian distribution, these models canbe updated using a mathematical approach including, for example, aBayesian approach. In such an embodiment, the updated model 1524, 1526can be determined based on the combination of prior model 1522 and thecalculated probability of the user providing the desired response to theprovided data packet.

In one embodiment, for example, the likelihood of the user providing thedesired response can be based on the user skill level and/or thedifficulty of the data packet, and can be determined using an ItemResponse Theory (“IRT”) model such as, for example, a Rasch model and/ora sigmoid and/or logistic curve. This probability of correctly answeringthe question can be input into, for example, a Gaussian model of thestudent skill level, which Gaussian model can be updated according to aBayesian technique to estimate a new student skill level.

After the additional data has been updated, the process 1400 proceeds todecision state 1412, wherein it is determined if there are anyadditional responses. In some embodiments, this can include determiningwhether there are any other responses received for the data packetidentified in block 1404. If there are additional responses, then theprocess returns to block 1402 and proceeds as outlined above. If thereare no additional responses then the process 1400 proceeds to block1414, wherein an alert is generated and sent to, for example, thesupervisor device 110. In some embodiments, this alert can be generatedand sent as described above. In some embodiments, this alert cancomprise information identifying, for example, the occurrence of theupdate to the attributes and/or data identifying the effect of theupdate.

In some embodiments, and in the place of providing the alert, theprocess 1400 at step 1414 can return to decision state 1206 and proceedas outlined above. Alternatively, in other embodiments, the process 1400at step 1414 can provide the data packet identified in block 1404 to oneor several users based on the updated attribute information.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application-specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein, the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read-only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system, comprising: a database coupled to a network and storing, in association: a question; a metadata including at least one attribute of the question; and a stability threshold defining a required number of responses to the question in order for the question to be identified as stable; a client device coupled to the network and including a user interface configured to receive a user input selecting the question and the metadata; a server, including a hardware computing device coupled to a network and including at least one processor executing within a memory instructions that, when executed, cause the system to: select, from the database the question and metadata selected from the client device; transmit the question to a plurality of user devices; receive a response from each of the plurality of user devices; select the stability threshold from the database; for each response received from the plurality of user devices: update the metadata reflecting an increase of a total number of responses by one; determine whether the total number of responses is equal to the stability threshold; and responsive to the total number of responses being equal to the stability threshold, generate a notification that the total number of responses is equal to the stability threshold.
 2. The system of claim 1, wherein the instructions further cause the system to: store, within the database, at least one user data characteristic stored in association with each of a plurality of users operating the plurality of user devices; and select the plurality of user devices according to a correlation between the metadata and the at least one user data characteristic.
 3. The system of claim 2, wherein the at least one user data characteristic includes a group, a class, a course of study, or a discipline common between the plurality of users.
 4. The system of claim 1, wherein, responsive to the total number of responses being equal to the threshold, the instructions cause the system to update the metadata to identify the question as stable.
 5. The system of claim 1, wherein, the instructions further cause the system to automatically transmit the notification through the network to a supervisor device operated by a supervisor user.
 6. The system of claim 5, wherein, the instructions further cause the system to: automatically launch a software application on the supervisor device that displays the notification and an indicator of metadata stability.
 7. A system, comprising: a server, including a hardware computing device coupled to a network and including at least one processor executing within a memory instructions that, when executed, cause the system to: select, from a database coupled to the network: a question; and a metadata including at least one attribute of the question; transmit the question to a plurality of user devices; receive a response from each of the plurality of user devices; select, from the database, a stability threshold defining a required number of responses to the question in order for the question to be identified as stable; for each response received from the plurality of user devices: update the metadata reflecting an increase of a total number of responses by one; determine whether the total number of responses is equal to the stability threshold; and responsive to the total number of responses being equal to the stability threshold, generate a notification that the total number of responses is equal to the stability threshold.
 8. The system of claim 7, wherein the instructions further cause the system to define a level of difficulty of the question within the metadata.
 9. The system of claim 8, wherein the instructions further cause the system to: generate a model stored in a model database coupled to the network; and update the model to update the difficulty level according to each response received from the plurality of devices.
 10. The system of claim 7, wherein the instructions further cause the system to determine whether the response is a correct response or an incorrect response to the question.
 11. The system of claim 7, wherein the database comprises a content library database.
 12. A method, comprising the steps of: selecting, by a server including a hardware computing device coupled to a network and including at least one processor executing instructions within a memory, from a database coupled to the network: a question; and a metadata including at least one attribute of the question; transmitting, by the server, the question to a plurality of user devices; receiving, by the server a response from each of the plurality of user devices; selecting, by the server from the database, a stability threshold defining a required number of responses to the question in order for the question to be identified as stable; for each response received from the plurality of user devices: updating, by the server, the metadata reflecting an increase of a total number of responses by one; determining, by the server, whether the total number of responses is equal to the stability threshold; and responsive to the total number of responses being equal to the stability threshold, generating, by the server, a notification that the total number of responses is equal to the stability threshold.
 13. The method of claim 12, further comprising the step of defining, by the server, a level of difficulty of the question within the metadata.
 14. The method of claim 13, further comprising the steps of: generating, by the server, a model stored in a model database coupled to the network; and updating, by the server, the model to update the difficulty level according to each response received from the plurality of devices.
 15. The method of claim 12, further comprising the steps of: storing, by the server within the database, at least one user data characteristic stored in association with each of a plurality of users operating the plurality of user devices; and selecting, by the server, the plurality of user devices according to a correlation between the metadata and the at least one user data characteristic.
 16. The method of claim 15, wherein the at least one user data characteristic includes a group, a class, a course of study, or a discipline common between the plurality of users.
 17. The method of claim 12, further comprising the step of determining, by the server, whether the response is a correct response or an incorrect response to the question.
 18. The method of claim 12, further comprising the step of, responsive to the total number of responses being equal to the threshold, updating, by the server, the metadata to identify the question as stable.
 19. The method of claim 12, further comprising the step of automatically transmitting, by the server, the notification through the network to a supervisor device operated by a supervisor user.
 20. The method of claim 19, further comprising the step of automatically launching, by the server, a software application on the supervisor device that displays the notification and an indicator of metadata stability. 